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Determining the number of static factors in approximate factor models       Matlab
Reference: Alessi, L., Barigozzi, M. and Capasso M.
Improved penalization for determining the number of factors in approximate static factor models
Statistics and Probability Letters, 2010, 80, 1806–1813

Structural dynamic factor model for the euro area       Matlab
Reference: Barigozzi, M., Conti, A., and Luciani, M.
Do euro area countries respond asymmetrically to the common monetary policy?
Oxford Bulletin of Economics and Statistics, 2014, 76, 693–714

nets       R package
Reference: Barigozzi, M. and Brownlees C.
NETS: Network estimation for time series

Dynamic factor models and volatilties       Matlab
Barigozzi, M. and Hallin, M.
Generalized dynamic factor models and volatilities: recovering the market volaitility shocks
Econometrics Journal, 2016, 19, C33–C60
Barigozzi, M. and Hallin, M.
Generalized dynamic factor models and volatilities: estimation and forecasting
Journal of Econometrics, 2017, 201, 307–321

Factors and networks for volatilties       Matlab
Reference: Barigozzi, M. and Hallin, M.
A network analysis of the volatility of high-dimensional financial series
Journal of the Royal Statistical Society - series C, 2017, 66(3), 581–605

Non-stationary dynamic factor models       Matlab
Reference: Barigozzi, M., Lippi, M., and Luciani, M.
Non-stationary dynamic factor models for large datasets
FEDS 2016-024, Board of Governors of the Federal Reserve System

factorcpt       R package
Reference: Barigozzi, M., Cho, H., and Fryzlewicz, P.
Simultaneous multiple change–point and factor analysis for high-dimensional time series

Factors and international financial markets       Matlab
Reference: Barigozzi, M., Hallin, M., and Soccorsi, S.
Identification of global and local shocks in international financial markets via general dynamic factor models
Journal of Financial Econometrics, 2018, forthcoming